High accuracy optical flow estimation based on a theory for warping

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3024, Issue , 2004, Pages 25-36

  Brox, Thomas ^a   Bruhn, Andrés ^a   Papenberg, Nils ^a   Weickert, Joachim ^a  

^a SAARLAND UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; COMPUTER VISION;

BRIGHTNESS CONSTANCIES; ENERGY FUNCTIONALS; FIXED POINT ITERATION; LARGE DISPLACEMENTS; NUMERICAL SCHEME; OPTICAL FLOW ESTIMATION; SMOOTHNESS CONSTRAINTS; THEORETICAL FOUNDATIONS;

OPTICAL FLOWS;

EID: 35048833329     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-24673-2_3     Document Type: Article

References (26)

1. L. Alvarez, J. Esclarín, M. Lefébure, and J. Sánchez. A PDE model for computing the optical flow. In Proc. XVI Congreso de Ecuaciones Diferenciales y Aplicaciones, pages 1349-1356, Las Palmas de Gran Canaria, Spain, Sept. 1999.
2. L. Alvarez, J. Weickert, and J. Sánchez. Reliable estimation of dense optical flow fields with large displacements. International Journal of Computer Vision, 39(1):41-56, Aug. 2000.
3. P. Anandan. A computational framework and an algorithm for the measurement of visual motion. International Journal of Computer Vision, 2:283-310, 1989.
4. A. Bab-Hadiashar and D. Suter. Robust optic flow computation. International Journal of Computer Vision, 29(1):59-77, Aug. 1998.
5. J. L. Barron, D. J. Fleet, and S. S. Beauchemin. Performance of optical flow techniques. International Journal of Computer Vision, 12(1):43-77, Feb. 1994.
6. M. J. Black and P. Anandan. Robust dynamic motion estimation over time. In Proc. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 292-302, Maui, HI, June 1991. IEEE Computer Society Press.
7. M. J. Black and P. Anandan. The robust estimation of multiple motions: parametric and piecewise smooth flow fields. Computer Vision and Image Understanding, 63(1):75-104, Jan. 1996.
8. I. Cohen. Nonlinear variational method for optical flow computation. In Proc. Eighth Scandinavian Conference on Image Analysis, volume 1, pages 523-530, Tromsø, Norway, May 1993.
9. R. Deriche, P. Kornprobst, and G. Aubert. Optical-flow estimation while preserving its discontinuities: a variational approach. In Proc. Second Asian Conference on Computer Vision, volume 2, pages 290-295, Singapore, Dec. 1995.
10. G. Farnebäck. Very high accuracy velocity estimation using orientation tensors, parametric motion, and simultaneous segmentation of the motion field. In Proc. Eighth International Conference on Computer Vision, volume 1, pages 171-177, Vancouver, Canada, July 2001. IEEE Computer Society Press.
11. B. Horn and B. Schunck. Determining optical flow. Artificial Intelligence, 17:185-203, 1981.
12. S. Ju, M. Black, and A. Jepson. Skin and bones: multi-layer, locally affine, optical flow and regularization with transparency. In Proc. 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 307-314, San Francisco, CA, June 1996. IEEE Computer Society Press.
13. S.-H. Lai and B. C. Vemuri. Reliable and efficient computation of optical flow. International Journal of Computer Vision, 29(2):87-105, Oct. 1998.
14. M. Lefébure and L. D. Cohen. Image registration, optical flow and local rigidity. Journal of Mathematical Imaging and Vision, 14(2):131-147, Mar. 2001.
15. B. Lucas and T. Kanade. An iterative image registration technique with an application to stereo vision. In Proc. Seventh International Joint Conference on Artificial Intelligence, pages 674-679, Vancouver, Canada, Aug. 1981.
16. E. Mémin and P. Pérez. A multigrid approach for hierarchical motion estimation. In Proc. Sixth International Conference on Computer Vision, pages 933-938, Bombay, India, Jan. 1998. Narosa Publishing House.
17. E. Mémin and P. Pérez. Hierarchical estimation and segmentation of dense motion fields. International Journal of Computer Vision, 46(2): 129-155, 2002.
18. H.-H. Nagel. Extending the 'oriented smoothness constraint' into the temporal domain and the estimation of derivatives of optical flow. In O. Faugeras, editor, Computer Vision - ECCV '90, volume 427 of Lecture Notes in Computer Science, pages 139-148. Springer, Berlin, 1990.
19. H.-H. Nagel and W. Enkelmann. An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8:565-593, 1986.
20. L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259-268, 1992.
21. C. Schnörr. Segmentation of visual motion by minimizing convex non-quadratic functionals. In Proc. Twelfth International Conference on Pattern Recognition, volume A, pages 661-663, Jerusalem, Israel, Oct. 1994. IEEE Computer Society Press.
22. M. Tistarelli. Multiple constraints for optical flow. In J.-O. Eklundh, editor, Computer Vision - ECCV '94, volume 800 of Lecture Notes in Computer Science, pages 61-70. Springer, Berlin, 1994.
23. S. Uras, F. Girosi, A. Verri, and V. Torre. A computational approach to motion perception. Biological Cybernetics, 60:79-87, 1988.
24. J. Weickert, A. Bruhn, and C. Schnörr. Lucas/Kanade meets Horn/Schunck: Combining local and global optic flow methods. Technical Report 82, Dept. of Mathematics, Saarland University, Saarbrücken, Germany, Apr. 2003.
25. J. Weickert and C. Schnörr. A theoretical framework for convex regularizers in PDE-based computation of image motion. International Journal of Computer Vision, 45(3):245-264, Dec. 2001.
26. J. Weickert and C. Schnörr. Variational optic flow computation with a spatio-temporal smoothness constraint. Journal of Mathematical Imaging and Vision, 14(3):245-255, May 2001.